SWE-bench Pro
SWE-bench Pro - refined benchmark with corrected problematic tasks in the public set. Internal agent scaffold; temp=1.0, top_p=0.95, 200K context window.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
47
What is SWE-bench Pro?
SWE-bench Pro is a harder, cleaned-up variant of SWE-bench: the task set was revised to remove flawed tasks from the original collection so scores are less distorted. As with SWE-bench Verified, an agent solves real GitHub issues from real repositories under defined conditions (temperature and context window are fixed).
What does it test?
SWE-bench Pro measures how well a model works as an autonomous coding agent. In detail, it checks:
- real bug fixing across multiple programming languages
- large, realistic code repositories instead of toy projects
- correct multi-file, multi-step changes by an autonomous agent
- clean task definitions without ambiguous or broken test setups
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the SWE-bench Pro benchmark?
SWE-bench Pro is a harder, cleaned-up variant of SWE-bench: the task set was revised to remove flawed tasks from the original collection so scores are less distorted. As with SWE-bench Verified, an agent solves real GitHub issues from real repositories under defined conditions (temperature and context window are fixed).
What does SWE-bench Pro test?
SWE-bench Pro mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: real bug fixing across multiple programming languages; large, realistic code repositories instead of toy projects; correct multi-file, multi-step changes by an autonomous agent.
How is the SWE-bench Pro score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get SWE-bench Pro data and scores?
The official source is https://huggingface.co/datasets/ScaleAI/SWE-bench_Pro. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/swe-bench-pro/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 47 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 26 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
49.5%
|
16.04.2026 | Qwen Team (official model card) | Verified |
| 27 |
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
|
46.9%
|
— | Report | Unverified |
| 28 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
44.6%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 29 |
Spark-X2.5-4B
SparkLLM (XHToken)
|
44.4%
|
— | Report | Verified |
| 30 |
Qwen3-Coder-Next
Qwen
|
44.3%
|
03.02.2026 | Report | Verified |
| 31 |
Ornith-1.0-9B
DeepReinforce AI
|
42.9%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 32 |
Kimi K2.5
Moonshot AI
|
40%
|
03.02.2026 | Report | Verified |
| 33 |
Gemma4-31B-it
Google
|
35.7%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 34 |
Gemma-4-31B
Google
|
35.7%
|
19.08.2026 | Report | Unverified |
| 35 |
Qwen3.5-4B
Qwen
|
29.4%
|
— | Report | Verified |
| 36 |
Gemma4-12B
Google
|
21.9%
|
25.06.2026 | Report | Verified |
| 37 |
MiniCPM5-2B
OpenBMB
|
14.4%
|
— | Report | Verified |
| 38 |
Gemma4-26B-A4B
Google
|
13.8%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 39 |
granite-4.2-3B
IBM
|
12.3%
|
— | Report | Verified |
| 40 |
Gemma 3 27B IT
Google DeepMind
|
11.38%
|
27.02.2025 | Report | Verified |
| 41 |
Spark-X2.5-1.7B
SparkLLM (XHToken)
|
10.4%
|
— | Report | Verified |
| 42 |
Gemma4-E4B
Google
|
4%
|
— | Report | Verified |
| 43 |
Qwen3.5-2B
Qwen Team (Alibaba Cloud)
|
1.9%
|
— | Report | Verified |
| 44 |
LFM2.5-2.6B
LiquidAI
|
0.6%
|
— | Report | Verified |
| 45 |
LFM2.5-8B-A1B
LiquidAI
|
0.4%
|
— | Report | Verified |
| 46 |
Nemotron-3-Nano-4B
NVIDIA
|
0.1%
|
— | Report | Verified |
| 47 |
Gemma4-E2B
Google
|
0%
|
— | Report | Verified |